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Join us Wednesday, September 9 to watch Techstars Starburst Space Accelerator demo day live

The 2020 class of Techstars Starburst Space Accelerator is graduating with an official demo day on Wednesday at 10 a.m. PDT (1 p.m. EDT), and you can watch all the teams present their startups live via the stream above. This year’s class includes 10 companies building innovative new solutions to challenges either directly or indirectly related to commercial space.

Techstars Starburst is a program with a lot of heavyweight backing from both private industry and public agencies, including from NASA’s JPL, the U.S. Air Force, Lockheed Martin, Maxar Technologies, SAIC, Israel Aerospace Industries North America and The Aerospace Corporation. The program, led by managing director Matt Kozlov, is usually based locally in LA, where much of the space industry has significant presence, but this year the demo day is going online due to the ongoing COVID-19 situation.

Few, if any, programs out there can claim such a broad representation of big-name partners from across commercial, military and general civil space in terms of stakeholders, which is the main reason it manages to attract a range of interesting startups.  This is the second class of graduating startups from the Starburst Space Accelerator; last year’s batch included some exceptional standouts like in-orbit refueling company Orbit Fab (also a TechCrunch Battlefield participant), imaging microsatellite company Pixxel and satellite propulsion company Morpheus.

As for this year’s class, you can check out a full list of all 10 participating companies below. The demo day presentations begin tomorrow, September 9 at 10 a.m. PDT/1 p.m. PDT, so you can check back in here then to watch live as they provide more details about what it is they do.

Bifrost

A synthetic data API that allows AI teams to generate their own custom datasets up to 99% faster — no tedious collection, curation or labelling required.
founders@bifrost.ai

Holos Inc.

A virtual reality content management system that makes it super easy for curriculum designers to create and deploy immersive learning experiences.
founders@holos.io

Infinite Composites Technologies

The most efficient gas storage systems in the universe.
founders@infinitecomposites.com

Lux Semiconductors

Lux is developing next generation System-on-Foil electronics.
founders@luxsemiconductors.com

Natural Intelligence Systems, Inc.

Developer of next-generation pattern-based AI/ML systems.
leadership@naturalintelligence.ai

Prewitt Ridge

Engineering collaboration software for teams building challenging deep tech projects.
founders@prewittridge.com

SATIM

Providing satellite radar-based intelligence for decision makers.
founders@satim.pl

Urban Sky

Developing stratospheric microballoons to capture the freshest, high-res earth observation data.
founders@urbansky.space

vRotors

Real-time remote robotic controls.
founders@vrotors.com

WeavAir

Proactive air insights.
founders@weavair.com

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Self-charging, thousand-year battery startup NDB aces key tests and lands first beta customers

Pleasanton-based green energy startup NDB, Inc. has reached a key milestone today with the completion of two proof of concept tests of its nano diamond battery (NDB) . One of these tests took place at the Lawrence Livermore National Laboratory, and the other at the Cavendish Laboratory at Cambridge University, and both saw NDB’s battery tech manage a 40% charge, which is a big improvement over the 15% charge collection efficiency (effectively energy lossiness relative to maximum total possible charge) of standard commercial diamond.

NDB’s innovation is in creating a new, proprietary nano diamond treatment that allows for more efficient extraction of electric charge from the diamond used in the creation of the battery. Their goal is to ultimately commercialize a version of their battery that can self-charge for up to a maximum lifespan of 28,000 years, created from artificial diamond-encased carbon-14 nuclear waste.

This battery doesn’t generate any carbon emissions in operation, and only requires access to open air to work. And while they’re technically batteries, because they contain a charge which will eventually be expended, they provide their own charge for much longer than the lifetime of any specific device or individual user, making them effectively a charge-free solution.

NDB ultimately hopes to turn their battery into a viable source of power for just about anything that consumes it — including aircraft, EVs, trains and more, all the way down to smartphones, wearables and tiny industrial sensors. The company is currently now at work creating a prototype of its first commercial battery in order to make that available sometime later this year.

It has also just signed its first beta customers, who will actually be receiving and making use of those first prototypes. While it hasn’t named them specifically, it did say that one is “a leader in nuclear fuel cycle products and services,” and the other is “a leading global aerospace, defense and security manufacturing company.” Obviously, this kind of tech has appeal in just about every sector, but defense and power concerns are likely among the deepest-pocketed.

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A pandemic and recession won’t stop Atlassian’s SaaS push

No company is completely insulated from the macroeconomic fallout of COVID-19, but we are seeing some companies fare better than others, especially those providing ways to collaborate online. Count Atlassian in that camp, as it provides a suite of tools focused on working smarter in a digital context.

At a time when many employees are working from home, Atlassian’s product approach sounds like a recipe for a smash hit. But in its latest earnings report, the company detailed slowing growth, not the acceleration we might expect. Looking ahead, it’s predicting more of the same — at least for the short term.

Part of the reason for that — beyond some small-business customers, hit by hard times, moving to its new free tier introduced last March — is the pain associated with moving customers off of older license revenue to more predictable subscription revenue. The company has shown that it is willing to sacrifice short-term growth to accelerate that transition.

We sat down with Atlassian CRO Cameron Deatsch to talk about some of the challenges his company is facing as it navigates through these crazy times. Deatsch pointed out that in spite of the turbulence, and the push to subscriptions, Atlassian is well-positioned with plenty of cash on hand and the ability to make strategic acquisitions when needed, while continuing to expand the recurring-revenue slice of its revenue pie.

The COVID-19 effect

Deatsch told us that Atlassian could not fully escape the pandemic’s impact on business, especially in April and May when many companies felt it. His company saw the biggest impact from smaller businesses, which cut back, moved to a free tier, or in some cases closed their doors. There was no getting away from the market chop that SMBs took during the early stages of COVID, and he said it had an impact on Atlassian’s new customer numbers.

Atlassian Q4FY2020 customer growth graph

Image Credits: Atlassian

Still, the company believes it will recover from the slow down in new customers, especially as it begins to convert a percentage of its new, free-tier users to paid users down the road. For this quarter it only translated into around 3000 new customers, but Deatsch didn’t seem concerned. “The customer numbers were off, but the overall financials were pretty strong coming out of [fiscal] Q4 if you looked at it. But also the number of people who are trying our products now because of the free tier is way up. We saw a step change when we launched free,” he said.

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Mission Bio raises $70 million to help scale its tech for improving the development of targeted cancer therapies

California-based startup Mission Bio has raised a new $70 million Series C funding round, led by Novo Growth and including participation from Soleus Capital and existing investors Mayfield, Cota and Agilent. Mission Bio will use the funding to scale its Tapestri Platform, which uses the company’s work in single-cell multi-omics technology to help optimize clinical trials for targeted, precision cancer therapies.

Mission Bio’s single-cell multi-omics platform is unique in the therapeutic industry. What it allows is the ability to zero in on a single cell, observing both genotype (fully genetic) and phenotype (observable traits influenced by genetics and other factors) impact resulting from use of various therapies during clinical trials. Mission’s Tapestri can detect both DNA and protein changes within the same single cell, which is key in determining effectiveness of targeted therapies because it can help rule out the effect of other factors not under control when analyzing in bulk (i.e. across groups of cells).

Founded in 2012 as a spin-out of research work conducted at UCSF, Mission Bio has raised a total of $120 million to date. The company’s tech has been used by a number of large pharmaceutical and therapeutic companies, including Agios, LabCorp and Onconova Therapeutics, as well as at cancer research centers including UCSF, Stanford and the Memorial Sloan Kettering Cancer Center.

In addition to helping with the optimization of clinical trials for treatments of blood cancers and tumors, Mission’s tech can be used to validate genome editing — a large potential market that could see a lot of growth over the next few years with the rise of CRISPR-based therapeutic applications.

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The race to building a fully functional quantum stack

David Cowan
Contributor

David Cowan is a partner at Bessemer Venture Partners and one of the world’s leading investors across cloud infrastructure, cybersecurity, consumer and space technology.

Tomer Diari
Contributor

Tomer Diari is a vice president at Bessemer Venture Partners, where he focuses primarily on cybersecurity, big data and deep tech opportunities.

Quantum computers exploit the seemingly bizarre yet proven nature of the universe that until a particle interacts with another, its position, speed, color, spin and other quantum properties coexist simultaneously as a probability distribution over all possibilities in a state known as superposition. Quantum computers use isolated particles as their most basic building blocks, relying on any one of these quantum properties to represent the state of a quantum bit (or “qubit”). So while classical computer bits always exist in a mutually exclusive state of either 0 (low energy) or 1 (high energy), qubits in superposition coexist simultaneously in both states as 0 and 1.

Things get interesting at a larger scale, as QC systems are capable of isolating a group of entangled particles, which all share a single state of superposition. While a single qubit coexists in two states, a set of eight entangled qubits (or “8Q”), for example, simultaneously occupies all 2^8 (or 256) possible states, effectively processing all these states in parallel. It would take 57Q (representing 2^57 parallel states) for a QC to outperform even the world’s strongest classical supercomputer. A 64Q computer would surpass it by 100x (clearly achieving quantum advantage) and a 128Q computer would surpass it a quintillion times.

In the race to develop these computers, nature has inserted two major speed bumps. First, isolated quantum particles are highly unstable, and so quantum circuits must execute within extremely short periods of coherence. Second, measuring the output energy level of subatomic qubits requires extreme levels of accuracy that tiny deviations commonly thwart. Informed by university research, leading QC companies like IBM, Google, Honeywell and Rigetti develop quantum engineering and error-correction methods to overcome these challenges as they scale the number of qubits they can process.

Following the challenge to create working hardware, software must be developed to harvest the benefits of parallelism even though we cannot see what is happening inside a quantum circuit without losing superposition. When we measure the output value of a quantum circuit’s entangled qubits, the superposition collapses into just one of the many possible outcomes. Sometimes, though, the output yields clues that qubits weirdly interfered with themselves (that is, with their probabilistic counterparts) inside the circuit.

QC scientists at UC Berkeley, University of Toronto, University of Waterloo, UT Sydney and elsewhere are now developing a fundamentally new class of algorithms that detect the absence or presence of interference patterns in QC output to cleverly glean information about what happened inside.

The QC stack

A fully functional QC must, therefore, incorporate several layers of a novel technology stack, incorporating both hardware and software components. At the top of the stack sits the application software for solving problems in chemistry, logistics, etc. The application typically makes API calls to a software layer beneath it (loosely referred to as a “compiler”) that translates function calls into circuits to implement them. Beneath the compiler sits a classical computer that feeds circuit changes and inputs to the Quantum Processing Unit (QPU) beneath it. The QPU typically has an error-correction layer, an analog processing unit to transmit analog inputs to the quantum circuit and measure its analog outputs, and the quantum processor itself, which houses the isolated, entangled particles.

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IoT and data science will boost foodtech in the post-pandemic era

Sunny Dhillon
Contributor

Sunny Dhillon is an early-stage investor at Signia Ventures in San Francisco where he invests in retail tech, e-commerce infrastructure and logistics, alongside consumer and enterprise software startups.

Even as e-grocery usage has skyrocketed in our coronavirus-catalyzed world, brick-and-mortar grocery stores have soldiered on. While strict in-store safety guidelines may gradually ease up, the shopping experience will still be low-touch and socially distanced for the foreseeable future.

This begs the question: With even greater challenges than pre-pandemic, how can grocers ensure their stores continue to operate profitably?

Just as micro-fulfillment centers (MFCs), dark stores and other fulfillment solutions have been helping e-grocers optimize profitability, a variety of old and new technologies can help brick-and-mortar stores remain relevant and continue churning out cash.

Today, we present three “must-dos” for post-pandemic retail grocers: rely on the data, rely on the biology and rely on the hardware.

Rely on the data

Image Credits: Pixabay/Pexels (opens in a new window)

The hallmark of shopping in a store is the consistent availability and wide selection of fresh items — often more so than online. But as the number of in-store customers continues to fluctuate, planning inventory and minimizing waste has become ever more so a challenge for grocery store managers. Grocers on average throw out more than 12% of their on-shelf produce, which eats into already razor-thin margins.

While e-grocers are automating and optimizing their fulfillment operations, brick-and-mortar grocers can automate and optimize their inventory planning mechanisms. To do this, they must leverage their existing troves of customer, business and external data to glean valuable insights for store managers.

Eden Technologies of Walmart is a pioneering example. Spun out of a company hackathon project, the internal tool has been deployed at over 43 distribution centers nationwide and promises to save Walmart over $2 billion in the coming years. For instance, if a batch of produce intended for a store hundreds of miles away is deemed soon-to-ripen, the tool can help divert it to the nearest store instead, using FDA standards and over 1 million images to drive its analysis.

Similarly, ventures such as Afresh Technologies and Shelf Engine have built platforms to leverage years of historical customer and sales data, as well as seasonality and other external factors, to help store managers determine how much to order and when. The results have been nothing but positive — Shelf Engine customers have increased gross margins by over 25% and Afresh customers have reduced food waste by up to 45%.

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Eight trends accelerating the age of commercial-ready quantum computing

Ethan Batraski
Contributor

Ethan Batraski is a partner at Venrock, where he invests across sectors with a particular focus on hard engineering problems such as developer infrastructure, advanced computing and space.

Every major technology breakthrough of our era has gone through a similar cycle in pursuit of turning fiction to reality.

It starts in the stages of scientific discovery, a pursuit of principle against a theory, a recursive process of hypothesis-experiment. Success of the proof of principle stage graduates to becoming a tractable engineering problem, where the path to getting to a systemized, reproducible, predictable system is generally known and de-risked. Lastly, once successfully engineered to the performance requirements, focus shifts to repeatable manufacturing and scale, simplifying designs for production.

Since theorized by Richard Feynman and Yuri Manin, quantum computing has been thought to be in a perpetual state of scientific discovery. Occasionally reaching proof of principle on a particular architecture or approach, but never able to overcome the engineering challenges to move forward.

That’s until now. In the last 12 months, we have seen several meaningful breakthroughs from academia, venture-backed companies, and industry that looks to have broken through the remaining challenges along the scientific discovery curve. Moving quantum computing from science fiction that has always been “five to seven years away,” to a tractable engineering problem, ready to solve meaningful problems in the real world.

Companies such as Atom Computing* leveraging neutral atoms for wireless qubit control, Honeywell’s trapped ions approach, and Google’s superconducting metals, have demonstrated first-ever results, setting the stage for the first commercial generation of working quantum computers.

While early and noisy, these systems, even at just 40-80 error-corrected qubit range, may be able to deliver capabilities that surpass those of classical computers. Accelerating our ability to perform better in areas such as thermodynamic predictions, chemical reactions, resource optimizations and financial predictions.

As a number of key technology and ecosystem breakthroughs begin to converge, the next 12-18 months will be nothing short of a watershed moment for quantum computing.

Here are eight emerging trends and predictions that will accelerate quantum computing readiness for the commercial market in 2021 and beyond:

1. Dark horses of QC emerge: 2020 will be the year of dark horses in the QC race. These new entrants will demonstrate dominant architectures with 100-200 individually controlled and maintained qubits, at 99.9% fidelities, with millisecond to seconds coherence times that represent 2x -3x improved qubit power, fidelity and coherence times. These dark horses, many venture-backed, will finally prove that resources and capital are not sole catalysts for a technological breakthrough in quantum computing.

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Activ Surgical raises $15 million to advance autonomous and collaborative robotic surgery

Boston-based startup Activ Surgical has raised a $15 million round of venture funding led by ARTIS Ventures, and including participation from LRVHealth, DNS Capital, GreatPoint Ventures, Tao Capital Partners and Rising Tide VC. The round will help Activ continue to develop and expand availability of its software platform, which it launched to market in May.

Activ Surgical’s ActivEdge platform uses data collected from surgical implements outfitted with sensors created by the company to collect real-time data during the actual surgical process. That data is then used to inform the development of machine learning and AI-based visualizations that can provide guidance to surgeons and surgical systems to help them reduce the occurrence of potential errors, and ultimately improve outcomes for patients.

The company’s primary aim is to bring technological innovation to the sphere of surgical vision, which still relies primarily on methods like using fluorescent dyes that date back more than 70 years. Activ wants to use computer vision to provide real-time visual insight into things that surgeons wouldn’t be able to see on their own — and ultimately to use those insights to power the next generation of both collaborative surgical robots and eventually even fully autonomous robotic surgical procedures.

ActivSight is the company’s first product in its ActivEdge platform offering, and is a small, connected imaging coddle that can be attached to existing laparoscopic and arthroscopic surgical instruments. The company is currently tracking toward getting their hardware cleared by the FDA for use by Q4 this year, and are working with eight hospital partners for pilot projects in the U.S.

The company has raised a total of $32 million in funding to date.

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Transparent face mask startup inhales $1M seed round

A Swiss startup called HMCARE, spun out of the École polytechnique fédérale de Lausanne, has raised a million Swiss Francs (equivalent to about $105 million) to commercialize its transparent and relatively eco-friendly surgical masks.

The founders were inspired by healthcare workers in the 2015 Ebola outbreak and at children’s hospitals around the world working closely with patients but unable to show their faces. Likewise parents and relatives of immunocompromised people who must make a human connection with two-thirds of their face covered.

There were technically transparent masks available, but they were just regular masks with a plastic window in them, which can fog up and isn’t breathable. Thierry Pelet, now CEO of the company, approached his EPFL colleagues with a prototype of a transparent mask material meeting the rigorous demands of a medical environment. It must permit air through but not viruses or bacteria, and so on.

The team worked with Swiss materials center Empa to create a new type of textile. Using biomass-derived transparent fibers placed 100 nanometers apart to form sheets and then triple-layered, they made a flexible, breathable material that’s also nearly transparent — a bit like lightly frosted glass. They call it the HelloMask.

The material can be made in bulk and formed into mask shapes just like normal cloth, but there is the matter of spinning up manufacturing for it. Fortunately, the world is desperate for masks, and the idea of a transparent one was clearly catnip for investors. HMCARE easily raised a million-franc seed round, the R&D work having been done using nonprofit donations and grants.

While the HelloMasks could launch as early as the start of 2021, they’ll be primarily for the medical community, though public availability is certainly a possibility.

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As wildfire season approaches, AI could pinpoint risky regions using satellite imagery

The U.S. has suffered from devastating wildfires over the last few years as global temperatures rise and weather patterns change, making the otherwise natural phenomenon especially unpredictable and severe. To help out, Stanford researchers have found a way to track and predict dry, at-risk areas using machine learning and satellite imagery.

Currently the way forests and scrublands are tested for susceptibility to wildfires is by manually collecting branches and foliage and testing their water content. It’s accurate and reliable, but obviously also quite labor intensive and difficult to scale.

Fortunately, other sources of data have recently become available. The European Space Agency’s Sentinel and Landsat satellites have amassed a trove of imagery of the Earth’s surface that, when carefully analyzed, could provide a secondary source for assessing wildfire risk — and one no one has to risk getting splinters for.

This isn’t the first attempt to make this kind of observation from orbital imagery, but previous efforts relied heavily on visual measurements that are “extremely site-specific,” meaning the analysis method differs greatly depending on the location. No splinters, but still hard to scale. The advance leveraged by the Stanford team is the Sentinel satellites’ “synthetic aperture radar,” which can pierce the forest canopy and image the surface below.

“One of our big breakthroughs was to look at a newer set of satellites that are using much longer wavelengths, which allows the observations to be sensitive to water much deeper into the forest canopy and be directly representative of the fuel moisture content,” said senior author of the paper, Stanford ecoydrologist Alexandra Konings, in a news release.

The team fed this new imagery, collected regularly since 2016, to a machine learning model along with the manual measurements made by the U.S. Forest Service. This lets the model “learn” what particular features of the imagery correlate with the ground-truth measurements.

They then tested the resulting AI agent (the term is employed loosely) by having it make predictions based on old data for which they already knew the answers. It was accurate, but most so in scrublands, one of the most common biomes of the American west and also one of the most susceptible to wildfires.

You can see the results of the project in this interactive map showing the model’s prediction of dryness at different periods all over the western part of the country. That’s not so much for firefighters as a validation of the approach — but the same model, given up to date data, can make predictions about the upcoming wildfire season that could help the authorities make more informed decisions about controlled burns, danger areas and safety warnings.

The researchers’ work was published in the journal Remote Sensing of Environment.

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